• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.48-58
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• 2009

In recent years, construction worker safety in construction site is important. Especially, the frequent collapse accidents have happened in tunnels, utility tunnel and underground structure, so that the importance of worker safety is greatly emphasized. It is difficult to communicate with other workers in underground space, using the current cable or wireless communicator. When the accident is occurred, it can't rescue workers. This is the reason that it has a deficiency to find a location of survivor and communicate rescure crew and field workers. In this paper we extract the optimal technical factors of USFSS(Underground Structure Field Support System) based on integrated technique of wireless communication and location awareness. And USFSS developed in this study is suited for bad environment of underground structure construction and able to track 3D position of laborer and communicate mutually.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.769-777
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• 2022

Purpose: This study analyzes and studies the characteristics of the Jeju area and uses them as basic data such as construction method design in the future development project. Method: Based on the ground survey data of the construction conducted in Jeju, the depth, relative density, N value, function state, color tone, groundwater level, and compressive strength were analyzed and studied. Result: Studies show that Jeju has columnar joints consisting of ancient volcanic activity and rapid cooling by nearby seawater, thick sand layers found on the coast, and clinker layers and Seogwipo layers formed by Mercury volcanic activity. Conclusion: It is hoped that it will be used as data for selecting basic design and basic construction method by understanding the special ground form of Jeju area and reflecting its characteristics well when designing construction.

• Journal of the Korean Geosynthetics Society
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• v.22 no.3
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• pp.105-117
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• 2023

The recent global energy supply crisis has led to increased uncertainty in international energy markets. These market changes lead to a rise in global energy prices and development is expanding to the extreme cold regions (Arctic Circle) where undeveloped energy resources are abundantly stored. Arctic Circle has a special business environment such as natural environment, laws, institutions and culture, research on location evaluation of development areas is necessary in advance. In this study, the spatial information of Alberta, Canada, where non-traditional energy resource development activities have recently been active, was collected and analyzed. In addition, an optimal location evaluation model for resource development was developed using construction environment spatial information data and the reliability is verified by comparing and analyzing the existing resource development areas.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.157-164
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• 2005

Urban conditions such as underground facilities and ambient noises due to cultural activity restrict the application of conventional geophysical techniques in general. We used the refraction microtremor (REMI) technique as an alternative way to get the geotechnical information, in particular shear-wave (S-wave) velocity information, at a site along an existing rail road. The REMI method uses ambient noises recorded using standard refraction equipment to derived shear-wave velocity information at a site. It does a wavefield transformation on the recorded wavefield to produce Rayleigh wave dispersion curve, which are then picked and modeled to get the shear-wave velocity structure. At this site the vibrations from the running trains provided strong noise sources that allowed REMI to be very effective. REMI was performed along the planned new underground rail tunnel. In addition, Suspension PS logging (SPS) were carried out at selected boreholes along the profile in order to draw out the quantitative relation between the shear wave velocity from the PS logging and the rock mass rating (RMR) determined from the inspection of the cores recovered from the same boreholes, These correlations were then used to relate the shear-wave velocity derived from REMI to RMR along the entire profile. The correlation between shear wave velocity and RMR was very good and so it was possible to estimate the RMR of the total zone of interest for the design of underground tunnel,

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.34-40
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• 2008

Underwater seismic refraction with advanced interpretation approaches makes important contributions to shallow marine exploration and geotechnical investigations in Australia's coastal areas. A series of case studies are presented to demonstrate the recent applications of continuous and static USR methods to river crossing and port infrastructure projects at various sites around Australia. In Sydney, static underwater seismic refraction (USR) with bottom-placed receivers and borehole seismic imaging assisted the development of improved geotechnical models that reduced construction risk for a tunnel crossing of the Lane Cove River. In Melbourne, combining conventional boomer reflection and continuous USR with near-bottom sources and receivers improved the definition of a buried, variably weathered basalt flow and assisted dredging assessment for navigation channel upgrades at Geelong Ports. Sand quality assessment with continuous USR and widely spaced borehole information assisted commercial decisions on available sand resources for the reclamation phase of development at the Port of Brisbane. Buried reefs and indurated layers occur in Australian coastal sediments with the characteristics of laterally limited, high velocity, cap layers within lower velocity materials. If these features are not recognised then significant error in depth determination to deeper refractors can occur. Application of advanced refraction inversion using wavefront eikonal tomography to continuous USR data obtained along the route of a proposed offshore pipeline near Fremantle allowed these layers and the underlying bedrock refractor to be accurately imaged. Static USR and the same interpretation approach was used to image the drowned granitic regolith beneath sediments and indurated layers in the northern area of Western Australia at a proposed new berthing site where deep piling was required. This allowed preferred piling sites to be identified, reducing overall pile lengths. USR can be expected to find increased application to shallow marine exploration and geotechnical investigations in Australia's coastal areas as economic growth continues and improved interpretation methods are developed.

• Tunnel and Underground Space
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• v.15 no.4 s.57
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• pp.275-287
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• 2005

At the planning and design stages of a development of underground space or tunneling project, the information regarding ground conditions is very important to enhance economical efficiency and overall safety In general, the information can be expressed using RMR or Q-system and with the geophysical exploration image. RMR or Q-system can provide direct information of rock mass in a local scale for the design scheme. Oppositely, the image of geophysical exploration can provide an exthaustive but indirect information. These two types of the information have inherent uncertainties from various sources and are given in different scales and with their own physical meanings. Recently, RMR has been estimated in unsampled areas based on given data using geostatistical methods like Kriging and conditional simulation. In this study, simulated annealing(SA) is applied to overcome the shortcomings of Kriging methods or conditional simulations just using a primary variable. Using this technique, RMR and the image of geophysical exploration can be integrated to construct the spatial distribution of RM and to evaluate its uncertainty. The SA method was applied to solve an optimization problem with constraints. We have suggested the practical procedure of the SA technique for the uncertainty evaluation of RMR and also demonstrated this technique through an application, where it was used to identify the spatial distribution of RMR and quantify the uncertainty. For a geotechnical application, the objective functions of SA are defined using statistical models of RMR and the correlations between RMR and the reference image. The applicability and validity of this application are examined and then the result of uncertainty evaluation can be used to optimize the tunnel layout.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.108-113
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• 2006

Urban conditions, such as existing underground facilities and ambient noise due to cultural activity, restrict the general application of conventional geophysical techniques. At a tunnelling site in an urban area along an existing railroad, we used the refraction microtremor (REMI) technique (Louie, 2001) as an alternative way to get geotechnical information. The REMI method uses ambient noise recorded by standard refraction equipment and a linear geophone array to derive a shear-wave velocity profile. In the inversion procedure, the Rayleigh wave dispersion curve is picked from a wavefield transformation, and iteratively modelled to get the S-wave velocity structure. The REMI survey was carried out along the line of the planned railway tunnel. At this site vibrations from trains and cars provided strong seismic sources that allowed REMI to be very effective. The objective of the survey was to evaluate the rock mass rating (RMR), using shear-wave velocity information from REMI. First, the relation between uniaxial compressive strength, which is a component of the RMR, and shear-wave velocity from laboratory tests was studied to learn whether shear-wave velocity and RMR are closely related. Then Suspension PS (SPS) logging was performed in selected boreholes along the profile, in order to draw out the quantitative relation between the shear-wave velocity from SPS logging and the RMR determined from inspection of core from the same boreholes. In these tests, shear-wave velocity showed fairly good correlation with RMR. A good relation between shear-wave velocity from REMI and RMR could be obtained, so it is possible to estimate the RMR of the entire profile for use in design of the underground tunnel.